A Cyanobacteria-Based Photosynthetic Process for th P d ti f Eth lthe Production of Ethanol

Ron Chance, Ben McCool, and John ColemanP t ti t N ti l R h C ilPresentation to National Research CouncilJune 13, 2011

Background — Algae Cultivation

• Microalgae/Cyanobacteria• Photoautotrophs—carry out photosynthesis (sunlight, CO2,

and water to organic molecules)M h hi h d ti it th l l t ( 30 )• Much higher productivity than larger plants (~30x)

• 10’s of thousands of known species including blue green algae (cyanobacteria)

• Commercial production of high value products

• Aquatic Species Program (NREL 1980-95)• Focus on bio-diesel production from harvested algae

Open Ponds (Raceway Design)Microalgae to High Value Products

Cyanotech, Big Island, Hawaii

• Favored Open Pond vs. Closed Systems• Pilot plant in New Mexico• Major issues with economics and contamination

• Current Status – Biofuel Production• Numerous algae-to-fuels companies• Almost all based on production from harvested algae• Mixture of open pond and closed systems• None with demonstrated commercial viability for biofuels Raceway Pond Design from

Aquatic Species Program

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Algenol Overview

• Algenol is an advanced industrial biotechnology company founded in 2006 Headquartered in Bonita Springs, Florida

2 CO2 + 3 H2O

C2H5OH + 3 O2

Research labs in Fort Myers, Florida and Berlin, Germany

120 employees and consultants including 60 PhDPhDs

• Algenol is developing its patented cyanobacteria-based technology platform for ethanol production Unique no harvest, Direct to Ethanol© technology

with low biomass waste $25MM DOE grant for Integrated Biorefinery

Direct To Ethanol® technology

o Project passed DOE Gate 1o Partnered with Dow Chemical, NREL,

Georgia Tech, Membrane Tech & Research, University of Colorado

Other key partners: Linde, Valero, Biofields, Honeywell

1000 L scale indoor experiments (Florida)

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Algenol Research and Development Facilities

• New Fort Myers, Florida facility which consolidates Algenol’s existing U.S. lab and outdoor testing facilities 40,000 ft2 lab space (biology, physiology, engineering)

4 acre o tdoor Process De elopment Unit (aq ac lt re) 4 acre outdoor Process Development Unit (aquaculture) 36 acres for pilot testing (17 acre DOE-funded pilot)

• Berlin labs (Cyano Biofuels, wholly-owned Algenol subsidiary) Screening of wild-type organisms Metabolic Engineering Lab to medium scale characterization of ethanol

productionFort Myers Laboratory (Completed August 2010)

production

Process Development Unit Commercial Scale PhotobioreactorsBerlin Laboratory Group Commercial Scale Photobioreactors

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Berlin Laboratory Group

DIRECT TO ETHANOL® Commercial Vision

Enhanced cyanobacteria, CO2 and solar energy to produce ethanol

2 CO2 + 3 H2O C2H5OH + 3 O22 CO2 + 3 H2O C2H5OH + 3 O2

CO2 can be sourced from:

• Power Plant• Refinery or Chem. Plant• Cement Plant

Closed photobioreactors (seawater)Very low freshwater consumption

Return Water

• Natural Gas Well• Ambient Air

y pNo-harvest strategyEthanol collected from vapor phase

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Biological Carbon Platform

Cyanobacteria (blue-green algae)• Fast-growing photosynthetic prokaryotes

with high rates of photo-conversion of CO2 into photosynthate and biomass

• Capacity for stable genetic enhancement with available molecular Dr. Heike Enke, enhancement with available molecular tools

• High rates of CO2/HCO3 assimilation in marine and freshwater environmentsD fi d i i th di ith

CSO, Berlin

• Defined inorganic growth medium with no organic C sources required

• Amenable for growth in enclosed photobioreactors• Wide range of growth forms and ecotypes among different generapWide range of growth forms and ecotypes among different genera

• Over 1500 curated strains from diverse environments available in-house within the Algenol Biofuels Culture Collection (ABCC)

• Screening program used to identify candidate species for genetic enhancement• Candidate species selected on the basis of numerous physiological morphological and• Candidate species selected on the basis of numerous physiological, morphological and

molecular criteria

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Metabolic Pathway for Ethanol Production

Enhanced cyanobacteria, photobioreactors, and ethanol separation systems are key, proprietary components of the Algenol technology.

2 CO2 + 3 H2O C2H5OH + 3 O2

Enhanced ethanol production via over-expression of genes for fermentation pathway enzymes

• These enzymes pyruvate decarboxylase (PDC) and alcohol dehydrogenase• These enzymes, pyruvate decarboxylase (PDC) and alcohol dehydrogenase(ADH), are found widely in nature.

• PDC catalyzes the non-oxidative decarboxylation of pyruvate to produce acetaldehyde.y

• ADH converts acetaldehyde to ethanol.• Ethanol diffuses from the cell into the culture medium and is collected without

the need to destroy the organism.

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Photosynthetic Efficiency and Productivity Targets

Ethanol Production Target• Algenol target is 6000 gal/acre-yr• Corn is about 400 gal/acre-yr; sugarcane about 1000• Corn is about 400 gal/acre-yr; sugarcane about 1000• Target corresponds to roughly 2% solar energy conversion

efficiency (referenced to average US solar radiation)• Efficiency similar to commercial biomass conversion for

Chlorella (food supplements) as well as conventional crops • Equivalent to about 20 g/m2 biomass production, with about

70 having been reported in the literature• Absolute theoretical limit (8 photons per C fixed or 24 for

Fort Myers Physiology Lab

• Absolute theoretical limit (8 photons per C fixed, or 24 for ethanol) is about 40,000 gal/acre-yr of ethanol or about 130 g/m2 of biomass

Potential Yield Limitations• Light (photosaturation, photoinhibition)• Diversion of fixed carbon to non-ethanologenic pathways

C t i t• Contaminants • Availability of CO2• Nutrient supply

Photosaturation Illustration

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(Melis, Plant Science, 2009)

Photobioreactor Technology

Enhanced cyanobacteria, photobioreactors, and ethanol separation systems are key, proprietary components of the Algenol technology.

• Algenol grows ethanologenic algae in patented photobioreactors (PBRs) which allow for optimum solar transmission and efficient ethanol collection

• Made of polyethylene with special additives and coatings to optimize performanceto optimize performance

• 4500 liter seawater culture• 15m long X 1.5m wide

• Ethanol-freshwater condensate is collected from photobioreactors and concentrated to feedstock-grade or fuel-grade ethanol using a combination of Algenol proprietary g p p yand conventional technology.

EtOH Separation EtOH Separation

First step in purification process is accomplished with solar energy and provides a clean ethanol freshwater solutionand provides a clean, ethanol-freshwater solution.

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Low Energy Mixing TechnologyK i d d• Keep organisms suspended

• Facilitate gas exchange • Ensure nutrient availabilityy

Experimental Techniques for Flow Visualization - Laser Sheet

Computational Methods for Mixing System Modeling and Next Flow Visualization Laser Sheet

VisualizationSystem Modeling and Next Generation Design – CFD

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Ethanol Purification Technology

Photo Bioreactor

Vapor Compression

Photo-Bioreactor

0.5 – 2 wt%

Vapor Compression Steam Stripping

5 – 20 wt%EtOH Separation EtOH Separation

Conventional Dist. Vapor Compression Dist.

Membrane Separation0 10

0.12

0.14

H (

atm

)

Ethanol vapor pressure

Ethanol-Water Phase Diagram

Mol Sieve Extract. Dist.

90 - 95 wt%

0.06

0.08

0.10

ress

ure

of

EtO

H

Raoult's law (γ = γ = 1)

Ethanol vapor pressureat 35°C

Membrane

99.7% Fuel Grade0.00

0.02

0.04

par

tial

pr Raoult s law (γe = γw = 1)

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0 0.2 0.4 0.6 0.8 1mole fraction of EtOH in water

Energy Demand for Direct to Ethanol® ProcessVCSS i l t E d d f i i• VCSS is a largest energy consumer

• Energy demand depends strongly on ethanol concentration in condensate*At 1% d t VCSS t

• Energy demand for mixing, gas delivery, pumping, etc. added in*

• Total system energy demand plotted vs condensate concentration for• At 1% condensate, VCSS represents

about 20% parasitic loadvs. condensate concentration for different dehydration options**

Energy Consumption Determined by Process Simulations (GaTech*,**)

0.50

0.60

MJ) 0.50

0.60

MJ/MJ) VCSS+Distillation+Mol Sieve

VCSS+Vapor Compression Distillation + Mol Sieve

(Input to Life Cycle Analysis)

0.30

0.40

onsumption (M

J/M

0.30

0.40

gy Con

sumption (M

VCSS + Membrane

0 00

0.10

0.20

VCSS En

ergy C

0 00

0.10

0.20otal Process Ene

rg

0.00

0.0 1.0 2.0 3.0 4.0 5.0 6.0wt% Ethanol in Condensate

0.00

0.0 1.0 2.0 3.0 4.0 5.0 6.0

T

wt% Ethanol in Condensate

*D. Luo, Z. Hu, D. Choi, V. Thomas, M. Realff, and R. Chance, Env. Sci. & Tech., 2010, 44 pp 8670–8677**D. Luo, Z. Hu, D. Choi, V. Thomas, M. Realff, B. McCool and R. Chance, unpublished results

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System Description for Life Cycle Analysis

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LCA for Evaluating Carbon Footprint and Technology OptionsTechnology Options

• LCA study is designed to be evergreen – continuously updated as part of our– continuously updated as part of our DOE project.

• Renewable Fuel Standard is met in all

Renewable Fuel Standard

Renewable Fuel Standard is met in all scenarios studied.

• LCA is important part of the evaluation p pof new technology options.

• Example: Polymer membrane technology (MTR), in combination with Algenol’s process simulations and integration p gconcepts, yields lower carbon footprint, as well as lower CAPEX and OPEX.

• Work on waste biomass disposition, to be performed at NREL, will be incorporated into LCA d t h i d lLCA and techno-economic model.

Life Cycle Analysis Appears in: D. Luo, Z. Hu, D. Choi, V. Thomas, M. Realff, and R. Chance, Env. Sci. & Tech., 2010, 44 pp 8670–8677. pp

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Energy Cost and Carbon Footprint Comparison

Transportation Fuel

Fuel energy (MJ/gal)

Production cost

($/gal)

Fuel to vehicle

efficiency (MJ/MJ)

Energy to vehicle(MJ/gal) Cents/MJ (vehicle)

GHGs Emissions (g-CO2/MJ (vehicle))

Gasoline 122.5 2.42 a 26%d 31.9 7.6 351 hGasoline 122.5 2.42 26% 31.9 7.6 351 Diesel 134.8 2.58 a 35%d 47.2 5.5 266 h

Corn Ethanol 80.5 2.61 b 26%d 20.9 12.5 275 h

Algenol Ethanol 80.5 1.37 c 26%d 20.9 6.6 55i

Grid to vehicle

$/grid-MJ

vehicle efficiency (MJ/MJ)

Energy to vehicle(MJ/grid-MJ) Cents/MJ (vehicle)

GHGs Emissions (g-CO2/MJ (vehicle))

Grid Electricity(Residential Sector) US Average 0 032 e 65% f,g 0 65 4 9 308 hSector) US Average 0.032 65% ,g 0.65 4.9 308

a U.S. Energy Information Administration, “Gasoline and Diesel Fuel Update”. http://www.eia.gov/oog/info/gdu/gasdiesel.asp (accessed Feb 2011)b U.S. Energy Information Administration, “Annual Energy Outlook 2007 with Projections to 203”0. http://www.eia.gov/oiaf/archive/aeo07/issues.html (accessed Feb 2011, Ethanol production prices adjusted for Corn and Natural Gas Market Prices as of Feb 2011)c Algenol’s Economic Model – Average production cost estimate with 6000 gal/acre-yr target (includes capital cost discounted over 15 years, and operating cost)dd Crabtree, G., Argonne National Laboratory, “Where is Transportation Going?” http://ceet.mccormick.northwestern.edu/events/domain_dinner07/crabtree_domain_12-3-07.pdf (accessed Feb 2011)e U.S. Energy Information Administration, “Monthly Electric Sales and Revenue Report with State Distributions Report, Form EIA-826”. http://www.eia.gov/cneaf/electricity/page/eia826.html (accessed Feb 2011)f Elgowainy et. al., Argonne National Laboratory, “Well-to-Wheels Analysis of Energy Use and GHG Emissions of Plug-In-Hybrid Electric Vehicles”. www transportation anl gov/pdfs/TA/559 pdf (accessed Feb 2011)www.transportation.anl.gov/pdfs/TA/559.pdf (accessed Feb 2011)g U.S. Department of Energy,” Fuel Economy”. www.fueleconomy.gov (accessed Feb 2011)h Argonne National Laboratory, ”Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model”. http://greet.es.anl.gov/ (accessed Feb 2011) [U.S. Mix Fuel assumed: Residual Oil (1.04%), Natural Gas (19.11%), Coal (47.24%), Nuclear Power (20.92%), Biomass (0.44%), Others (11.25%)]i D. Luo, Z. Hu, D. Choi, V. Thomas, M. Realff, and R. Chance, “Life Cycle Energy and Greenhouse Gas Emissions for an Ethanol Production Process Based on Blue-Green Algae” Env Sci & Tech 2010 44 pp 8670 8677

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Green Algae , Env. Sci. & Tech., 2010, 44 pp 8670–8677

Technical Partnerships/LeveragingKey Technical Areas Level of Effort/Other Contributions

PBRs; water treatment; process engineering; mixing systems

Over 20 scientists involved in DOE project and beyond

Gas management; product separation; process engineering; CO2 management; vapor liquid equilibria; gasification

6 scientists directly involved; facilitation of contacts with university scientists in gasification and VLE

Infrastructure and ethanol distribution Joint development exploring integration of Algenol process with refinery processes

Separations, life cycle analysis, CO2d li bi di iti

7 professors, 5 postdocs, 3 studentsdelivery, biomass disposition

Membrane separations, integration of the Bio-Sep system with our VCSS

3 scientists, extensive know-how in membrane applications

Biomass disposition and LCA support; 4 scientists extensive connections toBiomass disposition and LCA support;evaluation of industrial CO2 sources

4 scientists, extensive connections to algae science area

Ethanol separations via inorganic membranes

2 professors, 1 postdocmembranes

Control systems for DOE pilot facility and subsequent commercial projects

3 scientists, extensive science base in sensors and control instrumentation

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• Algenol Biofuels • Linde Group

Acknowledgements• Algenol Biofuels

• Algenol Engineering Group• Craig Smith • Ed Legere

• Linde Group• Hans Kinstenmacher• Mathias Mostertz• Martin Pottmann

• Paul Woods

• Dow Chemical Company• Steve Gluck

• Gerhard Lauermann

• Georgia Tech M tth R lff (ChBE)• Steve Gluck

• Ray Roach• Steve Tuttle• John Pendergast

D ncan Coffe

• Matthew Realff (ChBE)• Valerie Thomas (ISyE)• Bill Koros (ChBE)• Chris Jones (ChBE)

• Duncan Coffey• Todd Hogan• Jeff Bonekamp

• Sankar Nair (ChBE)• Victor Breedveld (ChBE)• Haiying Huang (CEE)

This material is based upon work supported in part by the Department of Energy under Award Number DE-FOA-0000096.

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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